Product Bundling Strategy for Office Supplies Retailer through Association Rules Mining: Comparative Study of Apriori and ECLAT Algorithms - Oetama, Raymond (AI Society & STMIK Indonesia, 2023)
Our study aims to develop an effective bundled product promotion strategy for the office supply store to boost sales. The primary challenge is comprehending which product combinations align with customer preferences and cater to their needs. We leverage the Apriori and ECLAT algorithms for consistent rule generation, revealing robust associations between product purchases. Notably, a strong positive correlation rule emerges at a confidence level of 0.8, while at 0.9, no results are found. The identical rules derived from both algorithms signify their reliability. The shop owner employs two rules for bundled products based on a minimum Lift Ratio of 1.96. The first bundle focuses on 70gsm natural paper in Folio and Quarto sizes, capitalizing on their popularity, even though customers may prefer one size. The second bundle emphasizes notebooks, often bought together but in smaller quantities than paper products, reflecting diverse customer needs and behaviors.